Bayer report

Dataset statistics

Number of variables	16
Number of observations	100116
Missing cells	0
Missing cells (%)	0.0%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	12.2 MiB
Average record size in memory	128.0 B

Variable types

Numeric	9
Categorical	7

Warnings

`id` is highly correlated with `customer_id`	High correlation
`customer_id` is highly correlated with `id`	High correlation
`is_cardiologist` is highly correlated with `is_gp`	High correlation
`is_gp` is highly correlated with `is_cardiologist`	High correlation
`gender_female` is highly correlated with `gender_male`	High correlation
`gender_male` is highly correlated with `gender_female`	High correlation
`office_or_hospital_based_Hospital` is highly correlated with `office_or_hospital_based_Office`	High correlation
`office_or_hospital_based_Office` is highly correlated with `office_or_hospital_based_Hospital`	High correlation
`id` is highly correlated with `customer_id`	High correlation
`customer_id` is highly correlated with `id`	High correlation
`is_cardiologist` is highly correlated with `is_gp`	High correlation
`is_gp` is highly correlated with `is_cardiologist`	High correlation
`gender_female` is highly correlated with `gender_male`	High correlation
`gender_male` is highly correlated with `gender_female`	High correlation
`office_or_hospital_based_Hospital` is highly correlated with `office_or_hospital_based_Office`	High correlation
`office_or_hospital_based_Office` is highly correlated with `office_or_hospital_based_Hospital`	High correlation
`id` is highly correlated with `customer_id`	High correlation
`customer_id` is highly correlated with `id`	High correlation
`is_cardiologist` is highly correlated with `is_gp`	High correlation
`is_gp` is highly correlated with `is_cardiologist`	High correlation
`gender_female` is highly correlated with `gender_male`	High correlation
`gender_male` is highly correlated with `gender_female`	High correlation
`office_or_hospital_based_Hospital` is highly correlated with `office_or_hospital_based_Office`	High correlation
`office_or_hospital_based_Office` is highly correlated with `office_or_hospital_based_Hospital`	High correlation
`is_gp` is highly correlated with `rep_id` and 3 other fields	High correlation
`rep_id` is highly correlated with `is_gp` and 1 other fields	High correlation
`id` is highly correlated with `customer_id`	High correlation
`gender_male` is highly correlated with `gender_female`	High correlation
`office_or_hospital_based_Office` is highly correlated with `is_gp` and 2 other fields	High correlation
`customer_id` is highly correlated with `id`	High correlation
`gender_female` is highly correlated with `gender_male`	High correlation
`office_or_hospital_based_Hospital` is highly correlated with `is_gp` and 2 other fields	High correlation
`is_cardiologist` is highly correlated with `is_gp` and 3 other fields	High correlation
`is_gp` is highly correlated with `is_cardiologist`	High correlation
`gender_female` is highly correlated with `gender_male`	High correlation
`office_or_hospital_based_Hospital` is highly correlated with `office_or_hospital_based_Office`	High correlation
`gender_male` is highly correlated with `gender_female`	High correlation
`office_or_hospital_based_Office` is highly correlated with `office_or_hospital_based_Hospital`	High correlation
`is_cardiologist` is highly correlated with `is_gp`	High correlation
`id` is uniformly distributed	Uniform
`customer_id` is uniformly distributed	Uniform
`email_open_total` has 36890 (36.8%) zeros	Zeros
`f2f_total` has 33974 (33.9%) zeros	Zeros
`prescription_total` has 86554 (86.5%) zeros	Zeros
`webinar_total` has 93792 (93.7%) zeros	Zeros

Reproduction

Analysis started	2021-05-28 21:04:16.265159
Analysis finished	2021-05-28 21:04:34.087162
Duration	17.82 seconds
Software version	pandas-profiling v3.0.0
Download configuration	config.json

id
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct	8343
Distinct (%)	8.3%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	4172.636821

Minimum	0
Maximum	8348
Zeros	12
Zeros (%)	< 0.1%
Negative	0
Negative (%)	0.0%
Memory size	782.3 KiB

Quantile statistics

Minimum	0
5-th percentile	417
Q1	2085
median	4172
Q3	6261
95-th percentile	7929
Maximum	8348
Range	8348
Interquartile range (IQR)	4176

Descriptive statistics

Standard deviation	2410.077866
Coefficient of variation (CV)	0.5775910938
Kurtosis	-1.200370264
Mean	4172.636821
Median Absolute Deviation (MAD)	2088
Skewness	0.0005144702163
Sum	417747708
Variance	5808475.318
Monotonicity	Increasing

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	12	< 0.1%
5480	12	< 0.1%
1322	12	< 0.1%
7465	12	< 0.1%
5416	12	< 0.1%
3307	12	< 0.1%
1258	12	< 0.1%
7401	12	< 0.1%
5352	12	< 0.1%
3243	12	< 0.1%
Other values (8333)	99996	99.9%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	12	< 0.1%
1	12	< 0.1%
2	12	< 0.1%
3	12	< 0.1%
4	12	< 0.1%
5	12	< 0.1%
6	12	< 0.1%
7	12	< 0.1%
8	12	< 0.1%
9	12	< 0.1%

Value	Count	Frequency (%)
8348	12	< 0.1%
8347	12	< 0.1%
8346	12	< 0.1%
8345	12	< 0.1%
8344	12	< 0.1%
8343	12	< 0.1%
8342	12	< 0.1%
8341	12	< 0.1%
8340	12	< 0.1%
8339	12	< 0.1%

customer_id
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct	8343
Distinct (%)	8.3%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	4272.636821

Minimum	100
Maximum	8448
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	782.3 KiB

Quantile statistics

Minimum	100
5-th percentile	517
Q1	2185
median	4272
Q3	6361
95-th percentile	8029
Maximum	8448
Range	8348
Interquartile range (IQR)	4176

Descriptive statistics

Standard deviation	2410.077866
Coefficient of variation (CV)	0.5640727182
Kurtosis	-1.200370264
Mean	4272.636821
Median Absolute Deviation (MAD)	2088
Skewness	0.0005144702163
Sum	427759308
Variance	5808475.318
Monotonicity	Increasing

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
2049	12	< 0.1%
3755	12	< 0.1%
7849	12	< 0.1%
5800	12	< 0.1%
3691	12	< 0.1%
1642	12	< 0.1%
7785	12	< 0.1%
5736	12	< 0.1%
3627	12	< 0.1%
1578	12	< 0.1%
Other values (8333)	99996	99.9%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
100	12	< 0.1%
101	12	< 0.1%
102	12	< 0.1%
103	12	< 0.1%
104	12	< 0.1%
105	12	< 0.1%
106	12	< 0.1%
107	12	< 0.1%
108	12	< 0.1%
109	12	< 0.1%

Value	Count	Frequency (%)
8448	12	< 0.1%
8447	12	< 0.1%
8446	12	< 0.1%
8445	12	< 0.1%
8444	12	< 0.1%
8443	12	< 0.1%
8442	12	< 0.1%
8441	12	< 0.1%
8440	12	< 0.1%
8439	12	< 0.1%

rep_id
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	153
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	154.2720844

Minimum	100
Maximum	252
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	782.3 KiB

Quantile statistics

Minimum	100
5-th percentile	102
Q1	124
median	149
Q3	185
95-th percentile	218
Maximum	252
Range	152
Interquartile range (IQR)	61

Descriptive statistics

Standard deviation	36.60864628
Coefficient of variation (CV)	0.2372992264
Kurtosis	-0.8447485583
Mean	154.2720844
Median Absolute Deviation (MAD)	29
Skewness	0.3773270901
Sum	15445104
Variance	1340.192982
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
102	4320	4.3%
145	2412	2.4%
191	2100	2.1%
147	2100	2.1%
125	2064	2.1%
129	1716	1.7%
151	1620	1.6%
124	1584	1.6%
105	1512	1.5%
153	1512	1.5%
Other values (143)	79176	79.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
100	1092	1.1%
101	948	0.9%
102	4320	4.3%
103	396	0.4%
104	372	0.4%
105	1512	1.5%
106	1476	1.5%
107	1428	1.4%
108	660	0.7%
109	348	0.3%

Value	Count	Frequency (%)
252	12	< 0.1%
251	12	< 0.1%
250	12	< 0.1%
249	12	< 0.1%
248	12	< 0.1%
247	36	< 0.1%
246	12	< 0.1%
245	12	< 0.1%
244	168	0.2%
243	12	< 0.1%

is_cardiologist
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	782.3 KiB

0	88080
1	12036

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	100116
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	88080	88.0%
1	12036	12.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	88080	88.0%
1	12036	12.0%

Most occurring characters

Value	Count	Frequency (%)
0	88080	88.0%
1	12036	12.0%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	100116	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	88080	88.0%
1	12036	12.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	100116	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	88080	88.0%
1	12036	12.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	100116	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	88080	88.0%
1	12036	12.0%

is_gp
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	782.3 KiB

1	88080
0	12036

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	100116
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	1
3rd row	1
4th row	1
5th row	1

Common Values

Value	Count	Frequency (%)
1	88080	88.0%
0	12036	12.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
1	88080	88.0%
0	12036	12.0%

Most occurring characters

Value	Count	Frequency (%)
1	88080	88.0%
0	12036	12.0%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	100116	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
1	88080	88.0%
0	12036	12.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	100116	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
1	88080	88.0%
0	12036	12.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	100116	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
1	88080	88.0%
0	12036	12.0%

years_since_graduation
Real number (ℝ_≥0)

Distinct	60
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	27.08845739

Minimum	3
Maximum	68
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	782.3 KiB

Quantile statistics

Minimum	3
5-th percentile	8
Q1	17
median	28
Q3	37
95-th percentile	46
Maximum	68
Range	65
Interquartile range (IQR)	20

Descriptive statistics

Standard deviation	12.36977458
Coefficient of variation (CV)	0.4566437433
Kurtosis	-0.9051889658
Mean	27.08845739
Median Absolute Deviation (MAD)	10
Skewness	0.007919249722
Sum	2711988
Variance	153.0113232
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
32	2964	3.0%
31	2952	2.9%
33	2940	2.9%
35	2928	2.9%
36	2856	2.9%
30	2832	2.8%
38	2736	2.7%
26	2652	2.6%
39	2640	2.6%
11	2616	2.6%
Other values (50)	72000	71.9%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
3	12	< 0.1%
4	504	0.5%
5	1032	1.0%
6	1608	1.6%
7	1812	1.8%
8	2388	2.4%
9	2424	2.4%
10	2220	2.2%
11	2616	2.6%
12	2124	2.1%

Value	Count	Frequency (%)
68	12	< 0.1%
63	24	< 0.1%
60	24	< 0.1%
59	60	0.1%
58	60	0.1%
57	48	< 0.1%
56	120	0.1%
55	192	0.2%
54	240	0.2%
53	288	0.3%

time_window_id
Real number (ℝ_≥0)

Distinct	12
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	6.5

Minimum	1
Maximum	12
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	782.3 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	3.75
median	6.5
Q3	9.25
95-th percentile	12
Maximum	12
Range	11
Interquartile range (IQR)	5.5

Descriptive statistics

Standard deviation	3.45206977
Coefficient of variation (CV)	0.5310876569
Kurtosis	-1.216784055
Mean	6.5
Median Absolute Deviation (MAD)	3
Skewness	0
Sum	650754
Variance	11.9167857
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=12)

Value	Count	Frequency (%)
1	8343	8.3%
2	8343	8.3%
3	8343	8.3%
4	8343	8.3%
5	8343	8.3%
6	8343	8.3%
7	8343	8.3%
8	8343	8.3%
9	8343	8.3%
10	8343	8.3%
Other values (2)	16686	16.7%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	8343	8.3%
2	8343	8.3%
3	8343	8.3%
4	8343	8.3%
5	8343	8.3%
6	8343	8.3%
7	8343	8.3%
8	8343	8.3%
9	8343	8.3%
10	8343	8.3%

Value	Count	Frequency (%)
12	8343	8.3%
11	8343	8.3%
10	8343	8.3%
9	8343	8.3%
8	8343	8.3%
7	8343	8.3%
6	8343	8.3%
5	8343	8.3%
4	8343	8.3%
3	8343	8.3%

conference_total
Categorical

Distinct	4
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	782.3 KiB

0	90525
1	9152
2	428
3	11

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	100116
Distinct characters	4
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	1
5th row	0

Common Values

Value	Count	Frequency (%)
0	90525	90.4%
1	9152	9.1%
2	428	0.4%
3	11	< 0.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	90525	90.4%
1	9152	9.1%
2	428	0.4%
3	11	< 0.1%

Most occurring characters

Value	Count	Frequency (%)
0	90525	90.4%
1	9152	9.1%
2	428	0.4%
3	11	< 0.1%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	100116	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	90525	90.4%
1	9152	9.1%
2	428	0.4%
3	11	< 0.1%

Most occurring scripts

Value	Count	Frequency (%)
Common	100116	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	90525	90.4%
1	9152	9.1%
2	428	0.4%
3	11	< 0.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	100116	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	90525	90.4%
1	9152	9.1%
2	428	0.4%
3	11	< 0.1%

email_open_total
Real number (ℝ_≥0)

ZEROS

Distinct	8
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.995505214

Minimum	0
Maximum	7
Zeros	36890
Zeros (%)	36.8%
Negative	0
Negative (%)	0.0%
Memory size	782.3 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	1
Q3	2
95-th percentile	3
Maximum	7
Range	7
Interquartile range (IQR)	2

Descriptive statistics

Standard deviation	0.9942147861
Coefficient of variation (CV)	0.9987037458
Kurtosis	0.913514897
Mean	0.995505214
Median Absolute Deviation (MAD)	1
Skewness	0.9843159179
Sum	99666
Variance	0.988463041
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=8)

Value	Count	Frequency (%)
1	36929	36.9%
0	36890	36.8%
2	18391	18.4%
3	6046	6.0%
4	1533	1.5%
5	286	0.3%
6	32	< 0.1%
7	9	< 0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	36890	36.8%
1	36929	36.9%
2	18391	18.4%
3	6046	6.0%
4	1533	1.5%
5	286	0.3%
6	32	< 0.1%
7	9	< 0.1%

Value	Count	Frequency (%)
7	9	< 0.1%
6	32	< 0.1%
5	286	0.3%
4	1533	1.5%
3	6046	6.0%
2	18391	18.4%
1	36929	36.9%
0	36890	36.8%

f2f_total
Real number (ℝ_≥0)

ZEROS

Distinct	23
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1.90197371

Minimum	0
Maximum	22
Zeros	33974
Zeros (%)	33.9%
Negative	0
Negative (%)	0.0%
Memory size	782.3 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	1
Q3	3
95-th percentile	6
Maximum	22
Range	22
Interquartile range (IQR)	3

Descriptive statistics

Standard deviation	2.155378666
Coefficient of variation (CV)	1.133232628
Kurtosis	5.601702041
Mean	1.90197371
Median Absolute Deviation (MAD)	1
Skewness	1.808003049
Sum	190418
Variance	4.645657194
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=23)

Value	Count	Frequency (%)
0	33974	33.9%
1	18825	18.8%
2	16083	16.1%
3	12435	12.4%
4	7979	8.0%
5	4653	4.6%
6	2516	2.5%
7	1449	1.4%
8	850	0.8%
9	480	0.5%
Other values (13)	872	0.9%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	33974	33.9%
1	18825	18.8%
2	16083	16.1%
3	12435	12.4%
4	7979	8.0%
5	4653	4.6%
6	2516	2.5%
7	1449	1.4%
8	850	0.8%
9	480	0.5%

Value	Count	Frequency (%)
22	2	< 0.1%
21	4	< 0.1%
20	5	< 0.1%
19	6	< 0.1%
18	18	< 0.1%
17	26	< 0.1%
16	29	< 0.1%
15	43	< 0.1%
14	69	0.1%
13	95	0.1%

prescription_total
Real number (ℝ_≥0)

ZEROS

Distinct	32
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.3535898358

Minimum	0
Maximum	42
Zeros	86554
Zeros (%)	86.5%
Negative	0
Negative (%)	0.0%
Memory size	782.3 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	2
Maximum	42
Range	42
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	1.291228808
Coefficient of variation (CV)	3.651770152
Kurtosis	112.2690004
Mean	0.3535898358
Median Absolute Deviation (MAD)	0
Skewness	7.950297873
Sum	35400
Variance	1.667271836
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=32)

Value	Count	Frequency (%)
0	86554	86.5%
1	4981	5.0%
2	4108	4.1%
3	1786	1.8%
4	1031	1.0%
5	565	0.6%
6	339	0.3%
7	199	0.2%
8	151	0.2%
9	99	0.1%
Other values (22)	303	0.3%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	86554	86.5%
1	4981	5.0%
2	4108	4.1%
3	1786	1.8%
4	1031	1.0%
5	565	0.6%
6	339	0.3%
7	199	0.2%
8	151	0.2%
9	99	0.1%

Value	Count	Frequency (%)
42	1	< 0.1%
40	1	< 0.1%
35	1	< 0.1%
34	2	< 0.1%
30	3	< 0.1%
29	5	< 0.1%
26	2	< 0.1%
25	2	< 0.1%
23	10	< 0.1%
22	3	< 0.1%

webinar_total
Real number (ℝ_≥0)

ZEROS

Distinct	10
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.08218466579

Minimum	0
Maximum	9
Zeros	93792
Zeros (%)	93.7%
Negative	0
Negative (%)	0.0%
Memory size	782.3 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	1
Maximum	9
Range	9
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	0.3676987394
Coefficient of variation (CV)	4.474055299
Kurtosis	69.75802141
Mean	0.08218466579
Median Absolute Deviation (MAD)	0
Skewness	6.790673379
Sum	8228
Variance	0.135202363
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=10)

Value	Count	Frequency (%)
0	93792	93.7%
1	5061	5.1%
2	876	0.9%
3	251	0.3%
4	67	0.1%
5	41	< 0.1%
7	14	< 0.1%
6	11	< 0.1%
8	2	< 0.1%
9	1	< 0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	93792	93.7%
1	5061	5.1%
2	876	0.9%
3	251	0.3%
4	67	0.1%
5	41	< 0.1%
6	11	< 0.1%
7	14	< 0.1%
8	2	< 0.1%
9	1	< 0.1%

Value	Count	Frequency (%)
9	1	< 0.1%
8	2	< 0.1%
7	14	< 0.1%
6	11	< 0.1%
5	41	< 0.1%
4	67	0.1%
3	251	0.3%
2	876	0.9%
1	5061	5.1%
0	93792	93.7%

gender_female
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	782.3 KiB

1	65616
0	34500

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	100116
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	1
3rd row	1
4th row	1
5th row	1

Common Values

Value	Count	Frequency (%)
1	65616	65.5%
0	34500	34.5%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
1	65616	65.5%
0	34500	34.5%

Most occurring characters

Value	Count	Frequency (%)
1	65616	65.5%
0	34500	34.5%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	100116	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
1	65616	65.5%
0	34500	34.5%

Most occurring scripts

Value	Count	Frequency (%)
Common	100116	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
1	65616	65.5%
0	34500	34.5%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	100116	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
1	65616	65.5%
0	34500	34.5%

gender_male
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	782.3 KiB

1	65616
0	34500

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	100116
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	1
3rd row	1
4th row	1
5th row	1

Common Values

Value	Count	Frequency (%)
1	65616	65.5%
0	34500	34.5%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
1	65616	65.5%
0	34500	34.5%

Most occurring characters

Value	Count	Frequency (%)
1	65616	65.5%
0	34500	34.5%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	100116	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
1	65616	65.5%
0	34500	34.5%

Most occurring scripts

Value	Count	Frequency (%)
Common	100116	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
1	65616	65.5%
0	34500	34.5%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	100116	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
1	65616	65.5%
0	34500	34.5%

office_or_hospital_based_Hospital
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	782.3 KiB

0	88980
1	11136

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	100116
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	88980	88.9%
1	11136	11.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	88980	88.9%
1	11136	11.1%

Most occurring characters

Value	Count	Frequency (%)
0	88980	88.9%
1	11136	11.1%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	100116	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	88980	88.9%
1	11136	11.1%

Most occurring scripts

Value	Count	Frequency (%)
Common	100116	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	88980	88.9%
1	11136	11.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	100116	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	88980	88.9%
1	11136	11.1%

office_or_hospital_based_Office
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	782.3 KiB

1	88980
0	11136

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	100116
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	1
3rd row	1
4th row	1
5th row	1

Common Values

Value	Count	Frequency (%)
1	88980	88.9%
0	11136	11.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
1	88980	88.9%
0	11136	11.1%

Most occurring characters

Value	Count	Frequency (%)
1	88980	88.9%
0	11136	11.1%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	100116	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
1	88980	88.9%
0	11136	11.1%

Most occurring scripts

Value	Count	Frequency (%)
Common	100116	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
1	88980	88.9%
0	11136	11.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	100116	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
1	88980	88.9%
0	11136	11.1%

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Count
Matrix

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

First rows

	customer_id	rep_id	is_gp	years_since_graduation	time_window_id	conference_total	email_open_total	f2f_total	prescription_total	webinar_total	gender_female	gender_male	office_or_hospital_based_Office
0	100	100	1	23	1	0	0	4	0	0	1	1	1
1	100	100	1	23	2	0	0	2	0	0	1	1	1
2	100	100	1	23	3	0	1	4	0	0	1	1	1
3	100	100	1	23	4	1	0	8	0	0	1	1	1
4	100	100	1	23	5	0	3	8	0	0	1	1	1
5	100	100	1	23	6	0	0	6	3	2	1	1	1
6	100	100	1	23	7	0	1	4	0	2	1	1	1
7	100	100	1	23	8	0	0	7	0	3	1	1	1
8	100	100	1	23	9	1	0	9	0	1	1	1	1
9	100	100	1	23	10	0	3	10	0	1	1	1	1

Last rows

	id	customer_id	rep_id	is_gp	years_since_graduation	time_window_id	email_open_total	f2f_total	prescription_total	gender_female	gender_male	office_or_hospital_based_Office
100106	8348	8448	115	1	3	3	0	0	0	1	1	1
100107	8348	8448	115	1	3	4	0	0	0	1	1	1
100108	8348	8448	115	1	3	5	0	0	0	1	1	1
100109	8348	8448	115	1	3	6	3	0	0	1	1	1
100110	8348	8448	115	1	3	7	0	0	0	1	1	1
100111	8348	8448	115	1	3	8	1	0	0	1	1	1
100112	8348	8448	115	1	3	9	0	1	1	1	1	1
100113	8348	8448	115	1	3	10	0	3	0	1	1	1
100114	8348	8448	115	1	3	11	1	4	0	1	1	1
100115	8348	8448	115	1	3	12	0	0	0	1	1	1

	customer_id	rep_id	is_gp	years_since_graduation	time_window_id	conference_total	email_open_total	f2f_total	prescription_total	webinar_total	gender_female	gender_male	office_or_hospital_based_Office
0	100	100	1	23	1	0	0	4	0	0	1	1	1
1	100	100	1	23	2	0	0	2	0	0	1	1	1
2	100	100	1	23	3	0	1	4	0	0	1	1	1
3	100	100	1	23	4	1	0	8	0	0	1	1	1
4	100	100	1	23	5	0	3	8	0	0	1	1	1
5	100	100	1	23	6	0	0	6	3	2	1	1	1
6	100	100	1	23	7	0	1	4	0	2	1	1	1
7	100	100	1	23	8	0	0	7	0	3	1	1	1
8	100	100	1	23	9	1	0	9	0	1	1	1	1
9	100	100	1	23	10	0	3	10	0	1	1	1	1

	customer_id	rep_id	is_gp	years_since_graduation	time_window_id	conference_total	email_open_total	f2f_total	prescription_total	webinar_total	gender_female	gender_male	office_or_hospital_based_Office
0	100	100	1	23	1	0	0	4	0	0	1	1	1
1	100	100	1	23	2	0	0	2	0	0	1	1	1
2	100	100	1	23	3	0	1	4	0	0	1	1	1
3	100	100	1	23	4	1	0	8	0	0	1	1	1
4	100	100	1	23	5	0	3	8	0	0	1	1	1
5	100	100	1	23	6	0	0	6	3	2	1	1	1
6	100	100	1	23	7	0	1	4	0	2	1	1	1
7	100	100	1	23	8	0	0	7	0	3	1	1	1
8	100	100	1	23	9	1	0	9	0	1	1	1	1
9	100	100	1	23	10	0	3	10	0	1	1	1	1

Overview

Variables

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

	customer_id	rep_id	is_gp	years_since_graduation	time_window_id	conference_total	email_open_total	f2f_total	prescription_total	webinar_total	gender_female	gender_male	office_or_hospital_based_Office
0	100	100	1	23	1	0	0	4	0	0	1	1	1
1	100	100	1	23	2	0	0	2	0	0	1	1	1
2	100	100	1	23	3	0	1	4	0	0	1	1	1
3	100	100	1	23	4	1	0	8	0	0	1	1	1
4	100	100	1	23	5	0	3	8	0	0	1	1	1
5	100	100	1	23	6	0	0	6	3	2	1	1	1
6	100	100	1	23	7	0	1	4	0	2	1	1	1
7	100	100	1	23	8	0	0	7	0	3	1	1	1
8	100	100	1	23	9	1	0	9	0	1	1	1	1
9	100	100	1	23	10	0	3	10	0	1	1	1	1